---
language: en
license: mit
library_name: pytorch
tags:
- mnist
- image-classification
- neural-network
datasets:
- mnist
metrics:
- accuracy
---

# Simple PyTorch Neural Network for MNIST

This model is a basic feed-forward neural network trained on the MNIST dataset as part of a PyTorch tutorial.

## Model Architecture
The model consists of:
1. **Input Layer**: 784 neurons (28x28 flattened images).
2. **Hidden Layer**: 128 neurons with ReLU activation.
3. **Output Layer**: 10 neurons (one for each digit from 0-9).

## Training Details
- **Dataset**: MNIST (60,000 training images, 10,000 test images)
- **Epochs**: 5 (by default)
- **Optimizer**: Adam (lr=0.001)
- **Loss Function**: CrossEntropyLoss

## Usage
To load this model in your PyTorch project:

```python
import torch
from simple_nn import SimpleNN

# 1. Initialize the model architecture
model = SimpleNN()

# 2. Load the state dictionary
model.load_state_dict(torch.load("model.pth"))
model.eval()
```

## Dataset Information
The MNIST dataset consists of 28x28 grayscale images of the 10 digits. It is a classic dataset for image classification tasks.